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Titel Referent Datum Ort

Mirlin, Rockstuhl, Schmalian, Schön, Shnirman

Mo 14.00-15.30


Rockstuhl, Schön

Di 12.30-14.00


Fr 15.45-17.15

Lehmann HS

Do 17.15-18.45

Information theory, machine learning, and the renormalization group

Seminar über Theoretische Festkörperphysik


Maciej Koch-Janusz


03.12.2018 14:00


Room 10.01, 10th Floor, Bldg. 30.23, KIT Campus South


ETH Zuerich


PD Dr. Igor Gornyi


Physical systems differing in their microscopic details often display
strikingly similar behaviour when probed at macroscopic scales. Those
universal properties, largely determining their physical
characteristics, are revealed by the renormalization group (RG)
procedure, which systematically retains ‘slow’ degrees of freedom and
integrates out the rest. We demonstrate a machine-learning algorithm
based on a model-independent, information-theoretic characterization of
real-space RG, capable of identifying the relevant degrees of freedom
and executing RG steps iteratively without any prior knowledge about
the system. We apply it to classical statistical physics problems in 1 and
2D: we demonstrate RG flow and extract critical exponents. We also
prove results about optimality of the procedure.